BI 089 Matt Smith: Drifting Cognition

Matt and I discuss how cognition and behavior drifts over the course of minutes and hours, and how global brain activity drifts with it. How does the brain continue to produce steady perception and action in the midst of such drift? We also talk about how to think about variability in neural activity. How much of it is noise and how much of it is hidden important activity? Finally, we discuss the effect of recording more and more neurons simultaneously, collecting bigger and bigger datasets, plus guest questions from Adam Snyder and Patrick Mayo. Smith Lab.Twitter: @SmithLabNeuro.Related:Slow drift of neural activity as a signature of impulsivity in macaque visual and prefrontal cortex.Artwork by Melissa Neely Take home points: The “noise” in the variability of neural activity is likely just activity devoted to processing other things.Recording lots of neurons simultaneously helps resolve the question of what’s noise and how much information is in a population of neurons.There’s a neural signature of the behavioral “slow drift” of our internal cognitive state.The neural signature is global, and it’s an open question how the brain compensates to produce steady perception and action. Timestamps: 0:00 - Intro 4:35 - Adam Snyder question  15:26 - Multi-electrode recordings  17:48 - What is noise in the brain?  23:55 - How many neurons is enough?  27:43 - Patrick Mayo question  33:17 - Slow drift  54:10 - Impulsivity  57:32 - How does drift happen?  59:49 - Relation to AI  1:06:58 - What AI and neuro can teach each other  1:10:02 - Ecologically valid behavior  1:14:39 - Brain mechanisms vs. mind  1:17:36 - Levels of description  1:21:14 - Hard things to make in AI  1:22:48 - Best scientific moment 

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Neuroscience and artificial intelligence work better together. Brain inspired is a celebration and exploration of the ideas driving our progress to understand intelligence. I interview experts about their work at the interface of neuroscience, artificial intelligence, cognitive science, philosophy, psychology, and more: the symbiosis of these overlapping fields, how they inform each other, where they differ, what the past brought us, and what the future brings. Topics include computational neuroscience, supervised machine learning, unsupervised learning, reinforcement learning, deep learning, convolutional and recurrent neural networks, decision-making science, AI agents, backpropagation, credit assignment, neuroengineering, neuromorphics, emergence, philosophy of mind, consciousness, general AI, spiking neural networks, data science, and a lot more. The podcast is not produced for a general audience. Instead, it aims to educate, challenge, inspire, and hopefully entertain those interested in learning more about neuroscience and AI.